Business Analytics: Descriptive, Predictive and Prescriptive

Business Analytics

1. Business Analytics

2. Introduction Chapter 1

3. Introduction (Slide 1 of 3) Three developments spurred recent explosive growth in the use of analytical methods in business applications: First development: Technological advances—scanner technology, data collection through e-commerce, Internet social networks, and data generated from personal electronic devices—produce incredible amounts of data for businesses. Businesses want to use these data to improve the efficiency and profitability of their operations, better understand their customers, price their products more effectively, and gain a competitive advantage.

4. Introduction (Slide 2 of 3) Three developments spurred recent explosive growth in the use of analytical methods in business applications (cont.): Second development: Ongoing research has resulted in numerous methodological developments, including: Advances in computational approaches to effectively handle and explore massive amounts of data. Faster algorithms for optimization and simulation. More effective approaches for visualizing data.

5. Introduction (Slide 3 of 3) Three developments spurred recent explosive growth in the use of analytical methods in business applications (cont.): Third development: The methodological developments were paired with an explosion in computing power and storage capability. Better computing hardware, parallel computing, and cloud computing have enabled businesses to solve big problems faster and more accurately than ever before.

6. Decision Making

7. Decision Making (Slide 1 of 4) Managers’ responsibility: To make strategic, tactical, or operational decisions. Strategic decisions : Involve higher-level issues concerned with the overall direction of the organization. Define the organization’s overall goals and aspirations for the future.

8. Decision Making (Slide 2 of 4) Tactical decisions: Concern how the organization should achieve the goals and objectives set by its strategy. Are usually the responsibility of midlevel management. Operational decisions: Affect how the firm is run from day to day. Are the domain of operations managers, who are the closest to the customer.

9. Decision Making (Slide 3 of 4) Decision making can be defined as the following process: Identify and define the problem. Determine the criteria that will be used to evaluate alternative solutions. Determine the set of alternative solutions. Evaluate the alternatives. Choose an alternative.

10. Decision Making (Slide 4 of 4) Common approaches to making decisions include: Tradition. Intuition. Rules of thumb. Using the relevant data available.

11. Business Analytics Defined

12. Business Analytics Defined (Slide 1 of 2) Business analytics : Scientific process of transforming data into insight for making better decisions. Used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making.

13. Business Analytics Defined (Slide 2 of 2) Tools of business analytics can aid decision making by: Creating insights from data. Improving our ability to more accurately forecast for planning. Helping us quantify risk. Yielding better alternatives through analysis and optimization.

14. A Categorization of Analytical Methods and Models Descriptive Analytics Predictive Analytics Prescriptive Analytics

15. A Categorization of Analytical Methods and Models (Slide 1 of 8) Descriptive Analytics: Descriptive analytics : Encompasses the set of techniques that describes what has happened in the past; examples include: Data queries. Reports. Descriptive statistics. Data visualization (including data dashboards). Data-mining techniques. Basic what-if spreadsheet models. Data query : A request for information with certain characteristics from a database.

16. A Categorization of Analytical Methods and Models (Slide 2 of 8) Descriptive Analytics (cont.): Data dashboards : Collections of tables, charts, maps, and summary statistics that are updated as new data become available. Uses of dashboards: To help management monitor specific aspects of the company’s performance related to their decision-making responsibilities. For corporate-level managers, daily data dashboards might summarize sales by region, current inventory levels, and other company-wide metrics. Front-line managers may view dashboards that contain metrics related to staffing levels, local inventory levels, and short-term sales forecasts.

17. A Categorization of Analytical Methods and Models (Slide 3 of 8) Descriptive Analytics (cont.): Data mining: The use of analytical techniques for better understanding patterns and relationships that exist in large data sets. Examples of data-mining techniques include: Cluster analysis. Sentiment analysis.

18. A Categorization of Analytical Methods and Models (Slide 4 of 8) Predictive Analytics: Predictive analytics: Consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another. Survey data and past purchase behavior may be used to help predict the market share of a new product.

19. A Categorization of Analytical Methods and Models (Slide 5 of 8) Predictive Analytics (cont.): Techniques used in Predictive Analytics include: Linear regression. Time series analysis. Data mining is u sed to find patterns or relationships among elements of the data in a large database; often used in predictive analytics. Simulation involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision.

20. A Categorization of Analytical Methods and Models (Slide 6 of 8) Prescriptive Analytics: Prescriptive Analytics : Indicates a best course of action to take: Provide a forecast or prediction, but do not provide a decision. A forecast or prediction, when combined with a rule, becomes a prescriptive model. Prescriptive models that rely on a rule or set of rules are often referred to as rule-based models.

21. A Categorization of Analytical Methods and Models (Slide 7 of 8) Prescriptive Analytics (cont.): Model Field Purpose Portfolio models Finance Use historical investment return data to determine the mix of investments that yield the highest expected return while controlling or limiting exposure to risk. Supply network design models Operations Provide the cost-minimizing plant and distribution center locations subject to meeting the customer service requirements. Price-markdown models Retailing Use historical data to yield revenue-maximizing discount levels and the timing of discount offers when goods have not sold as planned. Optimization models: Models that give the best decision subject to constraints of the situation.

22. A Categorization of Analytical Methods and Models (Slide 8 of 8) Prescriptive Analytics (cont.): Simulation optimization: Combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain settings. Decision analysis: Used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events. Employs utility theory , which assigns values to outcomes based on the decision maker’s attitude toward risk, loss, and other factors.

23. Big Data Volume Velocity Variety Veracity

24. Big Data (Slide 1 of 7) Big data : Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software. IBM describes the phenomenon of big data through the four Vs (as shown in Figure 1.1): Volume. Velocity. Variety. Veracity.

25. Big Data (Slide 2 of 7) Figure 1.1: The 4 Vs of Big Data Source: IBM

26. Big Data (Slide 3 of 7) Volume: Because data are collected electronically, we are able to collect more of it. To be useful, these data must be stored, and this storage has led to vast quantities of data. Velocity: Real-time capture and analysis of data present unique challenges both in how data are stored and the speed with which those data can be analyzed for decision making.

27. Big Data (Slide 4 of 7) Variety: More complicated types of data are now available and are proving to be of great value to businesses. Text data are collected by monitoring what is being said about a company’s products or services on social media platforms. Audio data are collected from service calls. Video data are collected by in-store video cameras and used to analyze shopping behavior. Analyzing information generated by these nontraditional sources is more complicated in part because of the processing required to transform the data into a numerical form that can be analyzed.

28. Big Data (Slide 5 of 7) Veracity: Veracity has to do with how much uncertainty is in the data. Inconsistencies in units of measure and the lack of reliability of responses in terms of bias also increase the complexity of the data.

29. Big Data (Slide 6 of 7) Represents opportunities. Presents challenges in terms of data storage and processing, security, and available analytical talent. The four Vs have led to new technologies: Hadoop: An open-source programming environment that supports big data processing through distributed storage and processing on clusters of computers. MapReduce: A programming model used within Hadoop that performs two major steps: the map step and the reduce step.

30. Big Data (Slide 7 of 7) Data security , the protection of stored data from destructive forces or unauthorized users, is of critical importance to companies. The complexities of the 4 Vs have increased the demand for analysts, but a shortage of qualified analysts has made hiring more challenging. More companies are searching for data scientists , who know how to process and analyze massive amounts of data. The Internet of Things (IoT) is the technology that allows data, collected from sensors in all types of machines, to be sent over the Internet to repositories where it can be stored and analyzed.

31. Business Analytics in Practice Financial Analytics Human Resource (HR) Analytics Marketing Analytics Health Care Analytics Supply-Chain Analytics Analytics for Government and Nonprofits Sports Analytics Web Analytics

32. Business Analytics in Practice (Slide 1 of 11) Figure 1.2: The Spectrum of Business Analytics Source: Adapted from SAS

33. Business Analytics in Practice (Slide 2 of 11) Predictive and prescriptive analytics are sometimes referred to as advanced analytics. Financial Analytics: Use of predictive models to: Forecast financial performance. Assess the risk of investment portfolios and projects. Construct financial instruments such as derivatives. Construct optimal portfolios of investments. Allocate assets. Create optimal capital budgeting plans. Simulation is also often used to assess risk in the financial sector.

34. Business Analytics in Practice (Slide 3 of 11) Human Resource (HR) Analytics: New area of application for analytics. The HR function is charged with ensuring that the organization: Has the mix of skill sets necessary to meet its needs. Is hiring the highest-quality talent and providing an environment that retains it. Achieves its organizational diversity goals.

35. Business Analytics in Practice (Slide 4 of 11) Marketing Analytics: Marketing is one of the fastest-growing areas for the application of analytics. A better understanding of consumer behavior through the use of scanner data and data generated from social media has led to an increased interest in marketing analytics.

36. Business Analytics in Practice (Slide 5 of 11) Marketing Analytics (cont.): A better understanding of consumer behavior through marketing analytics leads to: Better use of advertising budgets. More effective pricing strategies. Improved forecasting of demand. Improved product-line management. Increased customer satisfaction and loyalty.

37. Business Analytics in Practice (Slide 6 of 11) Health Care Analytics: Descriptive, predictive, and prescriptive analytics are used to improve: Patient, staff, and facility scheduling. Patient flow. Purchasing. Inventory control. Use of prescriptive analytics for diagnosis and treatment may prove to be the most important application of analytics in health care.

38. Business Analytics in Practice (Slide 7 of 11) Supply-Chain Analytics: The core service of companies such as UPS and FedEx is the efficient delivery of goods, and analytics has long been used to achieve efficiency. The optimal sorting of goods, vehicle and staff scheduling, and vehicle routing are all key to profitability for logistics companies such as UPS and FedEx. Companies can benefit from better inventory and processing control and more efficient supply chains.

39. Business Analytics in Practice (Slide 8 of 11) Analytics for Government and Nonprofits: Analytics for government to: Drive out inefficiencies. Increase the effectiveness and accountability of programs. Analytics for nonprofit agencies to ensure their effectiveness and accountability to their donors and clients.

40. Business Analytics in Practice (Slide 9 of 11) Sports Analytics Professional sports teams use to: Assess players for the amateur drafts. Decide how much to offer players in contract negotiations. Professional motorcycle racing teams use sophisticated optimization for gearbox design to gain competitive advantage. Teams use to assist with on-field decisions such as which pitchers to use in various games of a MLB playoff series.

41. Business Analytics in Practice (Slide 10 of 11) Sports Analytics (cont.): The use of analytics for off-the-field business decisions is increasing rapidly. Using prescriptive analytics, franchises across several major sports dynamically adjust ticket prices throughout the season to reflect the relative attractiveness and potential demand for each game.

42. Business Analytics in Practice (Slide 11 of 11) Web Analytics: The analysis of online activity, which includes, but is not limited to, visits to web sites and social media sites such as Facebook and LinkedIn. Leading companies apply descriptive and advanced analytics to data collected in online experiments to determine the best way to: Configure web sites. Position ads. Utilize social networks for the promotion of products and services.

43. Legal and Ethical Issues in the Use of Data and Analytics

44. Legal and Ethical Issues in the Use of Data and Analytics (Slide 1 of 4) Increased attention has been paid to ethical concerns around data privacy and the ethical use of models based on data. Companies have an obligation to protect the data and to not misuse that data. Clients and customers have an obligation to understand trade-offs between allowing their data to be collected, and the benefits they accrue from allowing a company to collect and use that data. An agreement must be signed between the customer and the company.

45. Legal and Ethical Issues in the Use of Data and Analytics (Slide 2 of 4) One of the strictest privacy laws is the General Data Protection Regulation. Went into effect in the European Union in May 2018. Stipulations: The request for consent to use an individual’s data must be easily understood and accessible. The intended use of data must be specified. Must be easy to withdraw consent. The individual has a right to a copy of their data and the right to demand their data be erased.

46. Legal and Ethical Issues in the Use of Data and Analytics (Slide 3 of 4) Analytics professionals have a responsibility to behave ethically. This includes protecting data, being transparent about the data and how it was collected, and what it does and does not contain. Analysts must be transparent about the methods used to analyze the data and any assumptions that have to be made for the methods used. Analysts must provide valid conclusions and understandable recommendations to their clients.

47. Legal and Ethical Issues in the Use of Data and Analytics (Slide 4 of 4) The American Statistical Association (ASA) and the Institute for Operations Research and the Management Sciences (INFORMS) provide ethical guidelines for analysts. The guidelines state that “Good statistical practice is fundamentally based on transparent assumptions, reproducible results, and valid interpretations.”

48. End of Chapter 1